基于现场PMU数据的实际输电系统机器学习状态估计
Machine Learning-Based State Estimation for an Actual Transmission System Using Field PMU Data
AI总结：
本文利用美国实际电网数据，评估深度神经网络在PMU不可观测输电系统中进行时间同步状态估计的可行性，系统分析其精度、可扩展性与计算性能。
AI中文摘要：
时间同步状态估计（SE）在确保现代电力系统实时态势感知方面发挥着关键作用。然而，由于成本和部署限制，使用相量测量单元（PMU）实现全系统可观测性往往不切实际。此外，在PMU时间尺度上的SE运行对延迟、鲁棒性和可靠性提出了严格要求，而在PMU不完全可观测的情况下，使用传统迭代混合SE技术难以满足这些要求。本文利用美国一家电力公司的实际数据，评估了在真实世界PMU不可观测输电系统中部署深度神经网络进行PMU时间尺度时间同步状态估计的可行性。主要贡献包括在现实运行条件下对估计精度、可扩展性和计算性能的系统评估。
英文摘要：
Time-synchronized state estimation (SE) plays a critical role in ensuring real-time situational awareness in modern power systems. However, achieving full system observability using phasor measurement units (PMUs) is often impractical due to cost and deployment constraints. Moreover, SE operation at PMU timescales imposes stringent requirements on latency, robustness, and reliability that are difficult to satisfy using conventional iterative hybrid SE techniques under incomplete observability by PMUs. This paper evaluates the feasibility of deploying deep neural networks for PMU-timescale, time-synchronized SE in real-world PMU-unobservable transmission systems using actual data from a US power utility. Key contributions include a systematic assessment of estimation accuracy, scalability, and computational performance under realistic operating conditions.
